terms

<code><a href="/link/terms()?package=seededlda&version=0.8.4" data-mini-rdoc="seededlda::terms()">terms()</a></code> returns the most likely terms, or words, for topics based on the <code>phi</code> parameter.

Implements the seeded-LDA model (Lu, Ott, Cardie & Tsou 2010) <doi:10.1109/ICDMW.2011.125> using the quanteda package and the GibbsLDA++ library for semisupervised topic modeling.
Seeded-LDA allows users to pre-define topics with keywords to perform theory-driven analysis of textual data in social sciences and humanities (Watanabe & Zhou 2020) <doi:10.1177/0894439320907027>.

Kohei Watanabe

seededlda

Seeded-LDA for Topic Modeling

Phan Xuan-Hieu

terms function

<dl><dt>x</dt>
<dd>a LDA model fitted by <code>textmodel_seededlda()</code> or <code>textmodel_lda()</code></dd>
<dt>n</dt>
<dd>number of terms to be extracted</dd></dl>

Arguments

<code><a href='https://rdrr.io/r/stats/terms.html'>terms()</a></code> returns the most likely terms, or words, for topics based on the <code>phi</code> parameter.

Extract most likely terms — terms

<dl>

<dt>x</dt>
<dd>a LDA model fitted by <code>textmodel_seededlda()</code> or <code>textmodel_lda()</code></dd>


<dt>n</dt>
<dd>number of terms to be extracted</dd>

</dl>

terms: Extract most likely terms

Description

Usage

Arguments

Details